带滑动窗口的SVM的概念漂移可视化
Concept Drift Visualization of SVM with Shifting Window
AI总结：
针对机器学习中概念漂移的可视化需求，提出基于平行坐标的时间平行直方图模型，结合带滑动窗口的增量/减量SVM，可检测并描绘概念漂移以解释模型决策。
摘要：
在机器学习领域，概念漂移是指信息的演化导致当前数据模型失效的现象，它发生在输入数据的统计特性随时间以不可预见的方式变化时。概念漂移检测在处理动态变化的数据时至关重要，其可视化能够为数据动态特性提供有价值的洞见，尤其适用于多维数据，且与视觉知识发现相关。我们提出了一种基于平行坐标的新型可视化模型，称为时间平行直方图（parallel histograms through time）。该模型表示连续时间偏移窗口下的特征分布直方图，通过连接连续时间窗口的分布均值得到的这些直方图的变化来展示漂移。我们展示了如何利用这些图表解释机器学习模型在选择漂移点时做出的决策。通过将漂移隔离在连续时间窗口的边缘，相邻窗口内将不存在（或减少）漂移。我们在合成数据集和真实数据集上都验证了这一概念。实验中，我们使用了我们在之前工作中提出的带滑动窗口的增量/减量SVM。借助我们提出的技术，除了检测概念漂移的存在，我们还能对其进行描绘，这些信息可进一步用于解释变化，为后续研究提供了可能。
英文摘要：
In machine learning, concept drift is an evolution of information that invalidates the current data model. It happens when the statistical properties of the input data change over time in unforeseen ways. Concept drift detection is crucial when dealing with dynamically changing data. Its visualization can bring valuable insight into the data dynamics, especially for multidimensional data, and is related to visual knowledge discovery. We propose a novel visualization model based on parallel coordinates, denoted as parallel histograms through time. Our model represents histograms of feature distributions for successive time-shifted windows. The drift is shown as variations of these histograms, obtained by connecting the means of the distribution for successive time windows. We show how these diagrams can be used to explain the decision made by the machine learning model in choosing the drift point. By isolating the drift at the edges of successive time windows, there will be none (or reduced) drift within the adjacent windows. We illustrate this concept on both synthetic and real datasets. In our experiments, we use an incremental/decremental SVM with shifting window, introduced by us in previous work. With our proposed technique, in addition to detect the presence of concept drift, we can also depict it. This information can be further used to explain the change. mental results, opening the possibility for further investigations.
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